Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller and high-quality synthetic datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are extremely computationally intensive as they require continuously optimizing a dataset among thousands of randomly initialized models. In this paper, we assume that training the synthetic data with diverse models leads to better generalization performance. Thus we propose two \textbf{model augmentation} techniques, ~\ie using \textbf{early-stage models} and \textbf{weight perturbation} to learn an informative synthetic set with significantly reduced training cost. Extensive experiments demonstrate that our method achieves up to 20$\times$ speedup and comparable performance on par with state-of-the-art baseline methods.

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter. Specifically, we compute the total updates by the exponentially decaying Average of the squared Updates (AU) on a parameter from the corresponding task.Based on this novel metric, we observe that many parameters in existing MTL methods, especially those in the higher shared layers, are still dominated by one or several tasks. The dominance of AU is mainly due to the dominance of accumulative gradients from one or several tasks. Motivated by this, we propose a Task-wise Adaptive learning rate approach, AdaTask in short, to separate the \emph{accumulative gradients} and hence the learning rate of each task for each parameter in adaptive learning rate approaches (e.g., AdaGrad, RMSProp, and Adam). Comprehensive experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks, resulting SOTA average task-wise performance. Analysis on both synthetic and real-world datasets shows AdaTask balance parameters in every shared layer well.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

由于自我批判性和歧义，了解动态的手动运动和动态动作是一项基本而又具有挑战性的任务。为了解决遮挡和歧义，我们开发了一个基于变压器的框架来利用时间信息以进行稳健的估计。注意到手部姿势估计和动作识别之间的不同时间粒度和语义相关性，我们建立了一个网络层次结构，其中有两个级联变压器编码器，其中第一个利用了短期的时间cue进行手姿势估算，而后者则每次聚集物，后者每次聚集体 - 帧姿势和对象信息在更长的时间范围内识别动作。我们的方法在两个第一人称手动作基准（即FPHA和H2O）上取得了竞争成果。广泛的消融研究验证了我们的设计选择。我们将开放源代码和数据以促进未来的研究。

人类运动建模对于许多现代图形应用非常重要，这些应用通常需要专业技能。为了消除外行的技能障碍，最近的运动生成方法可以直接产生以自然语言为条件的人类动作。但是，通过各种文本输入，实现多样化和细粒度的运动产生，仍然具有挑战性。为了解决这个问题，我们提出了MotionDiffuse，这是第一个基于基于文本模型的基于文本驱动的运动生成框架，该框架证明了现有方法的几种期望属性。 1）概率映射。 MotionDiffuse不是确定性的语言映射，而是通过一系列注入变化的步骤生成动作。 2）现实的综合。 MotionDiffuse在建模复杂的数据分布和生成生动的运动序列方面表现出色。 3）多级操作。 Motion-Diffuse响应有关身体部位的细粒度指示，以及随时间变化的文本提示，任意长度运动合成。我们的实验表明，Motion-Diffuse通过说服文本驱动运动产生和动作条件运动的运动来优于现有的SOTA方法。定性分析进一步证明了MotionDiffuse对全面运动产生的可控性。主页：https：//mingyuan-zhang.github.io/projects/motiondiffuse.html

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

采样在机器学习方法中无处不在。由于大数据集和模型复杂性的增长，我们希望在训练A表示时学习和适应采样过程。为了实现这一宏伟的目标，已经提出了各种抽样技术。但是，他们中的大多数要么使用固定采样方案，要么基于简单的启发式方法调整采样方案。他们不能选择在不同阶段进行模型培训的最佳样本。受认知科学中的“思考，快速和系统2）的启发，我们提出了一种奖励指导的采样策略，称为自适应样本，并奖励（ASR）来应对这一挑战。据我们所知，这是利用强化学习（RL）解决代表学习中抽样问题的第一项工作。我们的方法最佳地调整了采样过程以实现最佳性能。我们通过基于距离的采样来探索样品之间的地理关系，以最大程度地提高整体累积奖励。我们将ASR应用于基于相似性的损失函数中的长期抽样问题。信息检索和聚类中的经验结果证明了ASR在不同数据集中的出色性能。我们还讨论了一种令人着迷的现象，我们将其称为实验中的“ ASR重力”。

最近，基于生理信号的生物识别系统已受到广泛关注。与传统的生物特征特征不同，生理信号不容易被妥协（通常对人眼无法观察）。光杀解物学（PPG）信号易于测量，使其比许多其他用于生物特征验证的生理信号更具吸引力。但是，随着远程PPG（RPPG）的出现，当攻击者可以通过监视受害者的脸部远程窃取RPPG信号时，挑战不可观察，随后对基于PPG的生物识别构成威胁。在基于PPG的生物识别身份验证中，当前的攻击方法要求受害者的PPG信号，从而忽略了基于RPPG的攻击。在本文中，我们首先分析基于PPG的生物识别技术的安全性，包括用户身份验证和通信协议。我们评估了通过五种RPPG方法提取的信号波形，心率和脉冲间间隔信息，包括四种传统的光学计算方法（Chrom，POS，LGI，PCA）和一种深度学习方法（CL_RPPG）。我们在五个数据集（Pure，UBFC_RPPG，UBFC_PHYS，LGI_PPGI和COHFACE）上进行了实验，以收集一系列全面的结果集。我们的实证研究表明，RPPG对身份验证系统构成了严重威胁。用户身份验证系统中RPPG信号欺骗攻击的成功率达到0.35。基于脉冲间间隔的安全协议中的位命中率为0.6。此外，我们提出了一种积极的防御策略，以隐藏面部的生理信号以抵抗攻击。它将用户身份验证中RPPG欺骗攻击的成功率降低到0.05。位命中率降低到0.5，这是一个随机猜测的水平。我们的策略有效地阻止了PPG信号的暴露，以保护用户的敏感生理数据。